Occlusion prediction based on target dynamics and spatial reasoning

Teal, M.K.; Ellis, Tim

doi:10.1049/cp:19970887

Cited by 3 publications

(1 citation statement)

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“…Limited research has been performed in the area of occluded target tracking, because algorithm stability (track maintenance) [5] suffers from unobservable states which effects the fusion-feedback sensor control. To correct for unobservable target information, humans use natural physical understanding to assess target movements [1], imaging systems work with partial occluded targets [6][7][8][9], and the multitarget-multisensor ftision tracking community has yet to address the topic.…”

Section: Introductionmentioning

confidence: 99%

<title>Group tracking of occluded targets</title>

Connare

Blasch

2001

SPIE Proceedings

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Tracking algorithms typically rely on point-to-point correlation of sensor positional data. When target positional or identification data is obscured, missing, or noisy, tracking algorithm performance degrades and may lose track before a subsequent measurement is available. There are many cases when partial target detection results such as when a target travels behind an obstacle or is occluded by trees or industrial camouflage. In an effort to design tracking algorithms that can track through missing, occluded, or data dropout, we seek to use a group tracker technique to solve the occluded target tracking problem. Two methodologies are employed to compensate for a partially observable target state and covariance:(1) a coasting individual targets (CIT) method and (2) a group-updated track (GUT) method. The coasting method is analogous to the tracking prediction equations and the novel group tracking update method recovers the unobservable target position state from the other members of the group.

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Section: Introductionmentioning

confidence: 99%

<title>Group tracking of occluded targets</title>

Connare

Blasch

2001

SPIE Proceedings

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A new approach to multiple vehicle tracking in intersections using harris corners and adaptive background subtraction

Bragatto

Ruas

Benso

et al. 2008

2008 IEEE Intelligent Vehicles Symposium

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A Novel Method for Resolving Vehicle Occlusion in a Monocular Traffic-Image Sequence

Pang

Lam

Yung

2004

IEEE Trans. Intell. Transport. Syst.

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This paper presents a novel method for resolving the occlusion of vehicles seen in a sequence of traffic images taken from a single roadside mounted camera. Its concept is built upon a previously proposed vehicle-segmentation method, which is able to extract the vehicle shape out of the background accurately without the effect of shadows and other visual artifacts. Based on the segmented shape and that the shape can be represented by a simple cubical model, we propose a two-step method: first, detect the curvature of the shape contour to generate a data set of the vehicles occluded and, second, decompose it into individual vehicle models using a vanishing point in three dimensions and the set of curvature points of the composite model. The proposed method has been tested on a number of monocular traffic-image sequences and found that it detects the presence of occlusion correctly and resolves most of the occlusion cases involving two vehicles. It only fails when the occlusion was very severe. Further analysis of vehicle dimension also shows that the average estimation accuracy for vehicle width, length, and height are 94.78%, 94.09%, and 95.44%, respectively.

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Occlusion prediction based on target dynamics and spatial reasoning

Cited by 3 publications

References 0 publications

<title>Group tracking of occluded targets</title>

<title>Group tracking of occluded targets</title>

A new approach to multiple vehicle tracking in intersections using harris corners and adaptive background subtraction

A Novel Method for Resolving Vehicle Occlusion in a Monocular Traffic-Image Sequence

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